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Hierachical Bayesian Model to Validate
Unsupervised activity perception in crowded and
complicated scenes
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In unsupervised learning framework to model activities and
interactions in crowded and complicated scenes.
Under our framework hierarchical Bayesian models are used to
connect three elements in visual surveillance:
low-level visual features
simple atomic activities
Interactions
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Atomic activities are modeled as distributions over low-
level visual features, and multi-agent interactions are
modeled as distributions over atomic activities. These
models are learnt in an unsupervised way.
Given a long video sequence, moving pixels are clustered
into different atomic and short video clips are
clustered into different interactions.
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Maa-yoke Technology:
Maa-Yoke has a great deal of experience and knowledge and can
assist organizations in the following areas:
IT Service Management
IT Sales, Service & Support
Engineering
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SIFY
P&O NEDLOYD - PUNE
SONY INDIA LTD
FUTURE SOFT
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The Existing system fall in two categories namely:
The Object of interest is first detected and tracked
The Object is classified into categories.
Here the tracking is made by human so that error may occur
during the process.
Then video clips are made into image frames and the decided
object is tracked. When error done in few frames the result may
be concluded wrong.
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Next used approach is motion feature vectors to describe video
clip. The video clip is partition into bipartite graph.Here we dont use any detection or tracking, so an individual
activity cannot be separated so this lead to failure.
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In the current system we uses Hierarchical Bayesian Model which
has three models:
Latent Dirichlet Allocation(LDA)
Hierarchical Dirichlet Process (HDP) Mixture Model
Dual Hierarchical Dirichlet process (Dual - HDP) Model.
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Latent Dirichlet Allocation(LDA):
The LDA mixture model assumes that it is already known how
many different types of atomic activities and interactions occur in
the scene.
Hierarchical Dirichlet Process (HDP) Mixture Model:
The HDP mixture model automatically decides the number of
categories of atomic activities.
Dual Hierarchical Dirichlet process (Dual - HDP) Model.
The Dual-HDP automatically decides the numbers of categories
of both atomic activities and interactions.
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Hardware Requirements
Processor : Pentium-IV
Speed : 1.1GHz
RAM : 512MB
Hard Disk : 40GBGeneral : Key Board, Monitor, Mouse
Software Requirements
Operating System : Windows XPSoftware : JAVA (JDK 1.5.0)
Protocol : UDP
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Module Description:
There are four modules in our projects
Video Converted Into Frames
Find the Movable Object
Separation of Object
Apply the Rules in the Object
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Automatically segment a continuous video sequence.
Video Segment consists of number of pixel image frames
Slice the particular segment with a fixed rows and columns
segment the frames.
Identify the human objects
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To fix the pedestrian area or path to cross the movable
objects is called point safety place.
To compare each pixel frames and print the current
status of the pixel each moving pixel is labeled by its
location and direction of the objects.
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First step we can select the horizontal or vertical directory
paste it and load it. then separated it, and split it.A long
video sequence we can segment it based on different types
of interactions.
Our models provide a natural way to complete this task in anunsupervised since videos clips are automatically separated
into clusters.
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Apply the rules and find out the wrong user show the difference
between the vehicle and the human being based on the size and
speed.
we can get pedestrian area change movement and need to check if
the detected movements come in pedestrian area the human being
going in right path, or otherwise people going in wrong path.
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Segmentation Page
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To detect the abnormal activity in crowded place by
using Hierarchical Bayesian models
To overcome the systemize process in the crowded
places
To avoid the human error in crowded place activities
Example: Traffic signal is the best example of the project
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In this framework, we adopt the positions and moving
directions of moving pixels as low-level visual features since
they are more reliable in a crowded scene.
While we have demonstrated the effectiveness of this model in
a variety of visual surveillance tasks, including more
complicated features is expected to further boost the models
discrimination power.
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For example, if a pedestrian is walking along the path of
vehicles, just based on positions and moving detections, his
motions cannot be distinguished from those of vehicles and this
activity will not be detected as an abnormality.
If a car drives extremely fast, it will not be detected as
abnormal either. Other features, such as appearance and speed,
are useful in these scenarios.
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We have proposed an unsupervised framework adopting
hierarchical Bayesian models to model activities and
interactions in crowded and complicated scenes.
Three hierarchical Bayesian models: the LDA mixture model,
the HDP mixture model, and the Dual-HDP model are
proposed.
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Without tracking and human labeling, our system is able to
summarize typical activities and interactions in the scene,
segment the video sequences, detect typical and abnormal
activities, and support high-level semantic queries on activitiesand interactions. These surveillance tasks are formulated in an
integral probabilistic way.
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THANKYOU
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